Auswahl der wissenschaftlichen Literatur zum Thema „Nondominated sorting genetics algorithm (C-NSGA-II)“
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Zeitschriftenartikel zum Thema "Nondominated sorting genetics algorithm (C-NSGA-II)"
Maximov, Jordan, Galya Duncheva, Angel Anchev, Vladimir Dunchev, Vladimir Todorov und Yaroslav Argirov. „Influence of an Ageing Heat Treatment on the Mechanical Characteristics of Iron-Aluminium Bronzes with β-Transformation Obtained via Centrifugal Casting: Modelling and Optimisation“. Metals 13, Nr. 12 (24.11.2023): 1930. http://dx.doi.org/10.3390/met13121930.
Der volle Inhalt der QuelleZhang, Weipeng, Ke Wang und Chang Chen. „Artificial Neural Network Assisted Multiobjective Optimization of Postharvest Blanching and Drying of Blueberries“. Foods 11, Nr. 21 (25.10.2022): 3347. http://dx.doi.org/10.3390/foods11213347.
Der volle Inhalt der QuelleGong, Guiliang, Qianwang Deng, Xuran Gong, Like Zhang, Haibin Wang und He Xie. „A Bee Evolutionary Algorithm for Multiobjective Vehicle Routing Problem with Simultaneous Pickup and Delivery“. Mathematical Problems in Engineering 2018 (19.06.2018): 1–21. http://dx.doi.org/10.1155/2018/2571380.
Der volle Inhalt der QuelleSavsani, Vimal, Vivek Patel, Bhargav Gadhvi und Mohamed Tawhid. „Pareto Optimization of a Half Car Passive Suspension Model Using a Novel Multiobjective Heat Transfer Search Algorithm“. Modelling and Simulation in Engineering 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/2034907.
Der volle Inhalt der QuelleQu, Dan, Xianfeng Ding und Hongmei Wang. „An Improved Multiobjective Algorithm: DNSGA2-PSA“. Journal of Robotics 2018 (02.09.2018): 1–11. http://dx.doi.org/10.1155/2018/9697104.
Der volle Inhalt der QuelleZhang, Maoqing, Lei Wang, Zhihua Cui, Jiangshan Liu, Dong Du und Weian Guo. „Fast Nondominated Sorting Genetic Algorithm II with Lévy Distribution for Network Topology Optimization“. Mathematical Problems in Engineering 2020 (20.01.2020): 1–12. http://dx.doi.org/10.1155/2020/3094941.
Der volle Inhalt der QuelleLiu, Yi, Jun Guo, Huaiwei Sun, Wei Zhang, Yueran Wang und Jianzhong Zhou. „Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model“. Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/8215308.
Der volle Inhalt der QuelleXie, Yuan. „Fuzzy Parallel Machines Scheduling Problem Based on Genetic Algorithm“. Advanced Materials Research 204-210 (Februar 2011): 856–61. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.856.
Der volle Inhalt der QuelleDeng, Qianwang, Guiliang Gong, Xuran Gong, Like Zhang, Wei Liu und Qinghua Ren. „A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling“. Computational Intelligence and Neuroscience 2017 (2017): 1–20. http://dx.doi.org/10.1155/2017/5232518.
Der volle Inhalt der QuelleHou, Yaolong, Quan Yuan, Xueting Wang, Han Chang, Chenlin Wei, Di Zhang, Yanan Dong, Yijun Yang und Jipeng Zhang. „Parameter Design of a Photovoltaic Storage Battery Integrated System for Detached Houses Based on Nondominated Sorting Genetic Algorithm-II“. Buildings 14, Nr. 6 (17.06.2024): 1834. http://dx.doi.org/10.3390/buildings14061834.
Der volle Inhalt der QuelleDissertationen zum Thema "Nondominated sorting genetics algorithm (C-NSGA-II)"
Bouguila, Maissa. „Μοdélisatiοn numérique et οptimisatiοn des matériaux à changement de phase : applicatiοns aux systèmes cοmplexes“. Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR05.
Der volle Inhalt der QuellePhase-change materials exhibit considerable potential in the field of thermal management.These materials offer a significant thermal storage capacity. Excessive heat dissipated by miniature electronic components could lead to serious failures. A cooling system based on phase-change materials is among the most recommended solutions to guarantee the reliable performance of these microelectronic components. However, the low conductivity of these materials is considered a major limitation to their use in thermal management applications. The primary objective of this thesis is to address the challenge of improving the thermal conductivity of these materials. Numerical modeling is conducted, in the first chapters, to determine the optimal configuration of a heat sink, based on the study of several parameters such as fin insertion, nanoparticle dispersion, and the use of multiple phase-change materials. The innovation in this parametric study lies in the modeling of heat transfer from phase-change materials with relatively high nanoparticle concentration compared to the low concentration found in recent literature (experimental researchs). Significant conclusions are deducted from this parametric study, enabling us to propose a new model based on multiple phase-change materials improved with nanoparticles (NANOMCP). Reliable optimization studies are then conducted. Initially, a mono-objective reliability optimization study is carried out to propose a reliable and optimal model based on multiple NANOMCPs. The Robust Hybrid Method (RHM)proposes a reliable and optimal model, compared with the Deterministic Design Optimization method (DDO) and various Reliability Design Optimization methods (RBDO). Furthermore,the integration of a developed RBDO method (RHM) for the thermal management applicationis considered an innovation in recent literature. Additionally, a reliable multi-objective optimization study is proposed, considering two objectives: the total volume of the heat sink and the discharge time to reach ambient temperature. The RHM optimization method and the non-dominated sorting genetics algorithm (C-NSGA-II) were adopted to search for the optimal and reliable model that offers the best trade-off between the two objectives. Besides, An advanced metamodel is developed to reduce simulation time, considering the large number of iterations involved in finding the optimal model
Buchteile zum Thema "Nondominated sorting genetics algorithm (C-NSGA-II)"
Lee, Ki-Baek. „D-NSGA-II: Dual-Stage Nondominated Sorting Genetic Algorithm-II“. In Advances in Intelligent Systems and Computing, 291–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16841-8_27.
Der volle Inhalt der QuelleNguyen, Thi-Thu-Thuy, Po-Chang Ko, Ping-Chen Li, Ming-Hung Shu, Yuh-Shiuan Wu, Min-Zhi Li und Wen-Hsien Chen. „Pairs Trading Selection Using Nondominated Sorting Genetic Algorithm (NSGA-II)“. In Computational Intelligence Methods for Green Technology and Sustainable Development, 133–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19694-2_12.
Der volle Inhalt der QuelleGoudos, Sotirios K. „Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems“. In Multidisciplinary Computational Intelligence Techniques, 75–101. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1830-5.ch006.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Nondominated sorting genetics algorithm (C-NSGA-II)"
Lim, Jae Hyung, und Rolf D. Reitz. „High Load (21bar IMEP) Dual Fuel RCCI Combustion Using Dual Direct Injection“. In ASME 2013 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icef2013-19140.
Der volle Inhalt der QuellePatil, Pankaj, und Abhishek Abhishek. „Mission Based Design Optimization of Fixed Pitch Coaxial Propeller System for VTOL UAV“. In Vertical Flight Society 75th Annual Forum & Technology Display. The Vertical Flight Society, 2019. http://dx.doi.org/10.4050/f-0075-2019-14759.
Der volle Inhalt der QuelleLiu, Y., C. Zhou und W. J. Ye. „A fast optimization method of using nondominated sorting genetic algorithm (NSGA-II) and 1-nearest neighbor (1NN) classifier for numerical model calibration“. In 2005 IEEE International Conference on Granular Computing. IEEE, 2005. http://dx.doi.org/10.1109/grc.2005.1547351.
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